Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations1048532
Missing cells5155045
Missing cells (%)20.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 GiB
Average record size in memory1.2 KiB

Variable types

Numeric1
Categorical12
Text10
DateTime1

Alerts

Acceptance Status is highly overall correlated with Acceptance to Installation Days and 5 other fieldsHigh correlation
Acceptance to Installation Days is highly overall correlated with Acceptance StatusHigh correlation
Approval to Vendor Selection Days is highly overall correlated with Acceptance StatusHigh correlation
Inspection to Subsidy Redeemed Days is highly overall correlated with Acceptance StatusHigh correlation
Installation to Inspection Days is highly overall correlated with Acceptance StatusHigh correlation
Registration to Approval Days is highly overall correlated with Acceptance StatusHigh correlation
Vendor Selection to Acceptance Days is highly overall correlated with Acceptance StatusHigh correlation
Registration to Approval Days has 736435 (70.2%) missing values Missing
Approval to Vendor Selection Days has 736435 (70.2%) missing values Missing
Vendor Selection to Acceptance Days has 736435 (70.2%) missing values Missing
Acceptance to Installation Days has 736435 (70.2%) missing values Missing
Installation to Inspection Days has 736435 (70.2%) missing values Missing
Inspection to Subsidy Redeemed Days has 736435 (70.2%) missing values Missing
Subsidy Redeemed to Released Days has 736435 (70.2%) missing values Missing
Application Number has unique values Unique

Reproduction

Analysis started2025-03-26 09:08:19.067901
Analysis finished2025-03-26 09:09:45.242836
Duration1 minute and 26.17 seconds
Software versionydata-profiling vv4.15.1
Download configurationconfig.json

Variables

Application Number
Real number (ℝ)

Unique 

Distinct1048532
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55001552
Minimum10000007
Maximum99999843
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.0 MiB
2025-03-26T14:39:45.352846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10000007
5-th percentile14534332
Q132543755
median54969147
Q377494357
95-th percentile95506100
Maximum99999843
Range89999836
Interquartile range (IQR)44950602

Descriptive statistics

Standard deviation25969596
Coefficient of variation (CV)0.47216115
Kurtosis-1.1990249
Mean55001552
Median Absolute Deviation (MAD)22473804
Skewness0.0012504182
Sum5.7670887 × 1013
Variance6.7441991 × 1014
MonotonicityNot monotonic
2025-03-26T14:39:45.478017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67950044 1
 
< 0.1%
67744287 1
 
< 0.1%
42296617 1
 
< 0.1%
29500167 1
 
< 0.1%
63899773 1
 
< 0.1%
49616861 1
 
< 0.1%
62701673 1
 
< 0.1%
22728552 1
 
< 0.1%
79809291 1
 
< 0.1%
86157511 1
 
< 0.1%
Other values (1048522) 1048522
> 99.9%
ValueCountFrequency (%)
10000007 1
< 0.1%
10000035 1
< 0.1%
10000367 1
< 0.1%
10000419 1
< 0.1%
10000479 1
< 0.1%
10000487 1
< 0.1%
10000568 1
< 0.1%
10000594 1
< 0.1%
10000609 1
< 0.1%
10000612 1
< 0.1%
ValueCountFrequency (%)
99999843 1
< 0.1%
99999802 1
< 0.1%
99999784 1
< 0.1%
99999712 1
< 0.1%
99999657 1
< 0.1%
99999581 1
< 0.1%
99999548 1
< 0.1%
99999491 1
< 0.1%
99999482 1
< 0.1%
99999451 1
< 0.1%

Gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size53.9 MiB
Male
577239 
Female
461043 
Other
 
10250

Length

Max length6
Median length4
Mean length4.8891822
Min length4

Characters and Unicode

Total characters5126464
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 577239
55.1%
Female 461043
44.0%
Other 10250
 
1.0%

Length

2025-03-26T14:39:45.596470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-26T14:39:45.677062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 577239
55.1%
female 461043
44.0%
other 10250
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e 1509575
29.4%
a 1038282
20.3%
l 1038282
20.3%
M 577239
 
11.3%
F 461043
 
9.0%
m 461043
 
9.0%
O 10250
 
0.2%
t 10250
 
0.2%
h 10250
 
0.2%
r 10250
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5126464
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1509575
29.4%
a 1038282
20.3%
l 1038282
20.3%
M 577239
 
11.3%
F 461043
 
9.0%
m 461043
 
9.0%
O 10250
 
0.2%
t 10250
 
0.2%
h 10250
 
0.2%
r 10250
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5126464
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1509575
29.4%
a 1038282
20.3%
l 1038282
20.3%
M 577239
 
11.3%
F 461043
 
9.0%
m 461043
 
9.0%
O 10250
 
0.2%
t 10250
 
0.2%
h 10250
 
0.2%
r 10250
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5126464
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1509575
29.4%
a 1038282
20.3%
l 1038282
20.3%
M 577239
 
11.3%
F 461043
 
9.0%
m 461043
 
9.0%
O 10250
 
0.2%
t 10250
 
0.2%
h 10250
 
0.2%
r 10250
 
0.2%

State/UT
Categorical

Distinct36
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.9 MiB
Gujarat
195808 
Maharashtra
109263 
Uttar Pradesh
99285 
Rajasthan
86000 
Karnataka
60194 
Other values (31)
497982 

Length

Max length40
Median length17
Mean length9.887415
Min length3

Characters and Unicode

Total characters10367271
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGujarat
2nd rowUttarakhand
3rd rowMizoram
4th rowAndhra Pradesh
5th rowUttar Pradesh

Common Values

ValueCountFrequency (%)
Gujarat 195808
18.7%
Maharashtra 109263
 
10.4%
Uttar Pradesh 99285
 
9.5%
Rajasthan 86000
 
8.2%
Karnataka 60194
 
5.7%
Telangana 59912
 
5.7%
Andhra Pradesh 55539
 
5.3%
Madhya Pradesh 39994
 
3.8%
Tamil Nadu 34667
 
3.3%
Haryana 20386
 
1.9%
Other values (26) 287484
27.4%

Length

2025-03-26T14:39:45.785039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pradesh 211141
15.1%
gujarat 195808
14.0%
maharashtra 109263
 
7.8%
uttar 99285
 
7.1%
rajasthan 86000
 
6.1%
karnataka 60194
 
4.3%
telangana 59912
 
4.3%
andhra 55539
 
4.0%
and 41347
 
2.9%
madhya 39994
 
2.9%
Other values (37) 444235
31.7%

Most occurring characters

ValueCountFrequency (%)
a 2615452
25.2%
r 1045863
 
10.1%
h 823400
 
7.9%
t 724073
 
7.0%
n 507667
 
4.9%
s 502082
 
4.8%
d 496825
 
4.8%
e 361473
 
3.5%
354186
 
3.4%
u 319223
 
3.1%
Other values (33) 2617027
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10367271
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2615452
25.2%
r 1045863
 
10.1%
h 823400
 
7.9%
t 724073
 
7.0%
n 507667
 
4.9%
s 502082
 
4.8%
d 496825
 
4.8%
e 361473
 
3.5%
354186
 
3.4%
u 319223
 
3.1%
Other values (33) 2617027
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10367271
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2615452
25.2%
r 1045863
 
10.1%
h 823400
 
7.9%
t 724073
 
7.0%
n 507667
 
4.9%
s 502082
 
4.8%
d 496825
 
4.8%
e 361473
 
3.5%
354186
 
3.4%
u 319223
 
3.1%
Other values (33) 2617027
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10367271
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2615452
25.2%
r 1045863
 
10.1%
h 823400
 
7.9%
t 724073
 
7.0%
n 507667
 
4.9%
s 502082
 
4.8%
d 496825
 
4.8%
e 361473
 
3.5%
354186
 
3.4%
u 319223
 
3.1%
Other values (33) 2617027
25.2%
Distinct733
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size57.4 MiB
2025-03-26T14:39:46.134951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length26
Median length22
Mean length8.4202571
Min length3

Characters and Unicode

Total characters8828909
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNarmada
2nd rowPauri Garhwal
3rd rowKolasib
4th rowWest Godavari
5th rowDeoria
ValueCountFrequency (%)
north 19118
 
1.6%
south 17968
 
1.5%
west 11099
 
0.9%
east 10651
 
0.9%
sikkim 10608
 
0.9%
goa 10494
 
0.9%
mumbai 10417
 
0.9%
chandigarh 10156
 
0.8%
kachchh 9986
 
0.8%
mahisagar 9833
 
0.8%
Other values (736) 1093807
90.1%
2025-03-26T14:39:46.476528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1784338
20.2%
r 729616
 
8.3%
h 557852
 
6.3%
i 528349
 
6.0%
n 513719
 
5.8%
u 426407
 
4.8%
d 339034
 
3.8%
o 324505
 
3.7%
l 307012
 
3.5%
g 267837
 
3.0%
Other values (44) 3050240
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8828909
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1784338
20.2%
r 729616
 
8.3%
h 557852
 
6.3%
i 528349
 
6.0%
n 513719
 
5.8%
u 426407
 
4.8%
d 339034
 
3.8%
o 324505
 
3.7%
l 307012
 
3.5%
g 267837
 
3.0%
Other values (44) 3050240
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8828909
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1784338
20.2%
r 729616
 
8.3%
h 557852
 
6.3%
i 528349
 
6.0%
n 513719
 
5.8%
u 426407
 
4.8%
d 339034
 
3.8%
o 324505
 
3.7%
l 307012
 
3.5%
g 267837
 
3.0%
Other values (44) 3050240
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8828909
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1784338
20.2%
r 729616
 
8.3%
h 557852
 
6.3%
i 528349
 
6.0%
n 513719
 
5.8%
u 426407
 
4.8%
d 339034
 
3.8%
o 324505
 
3.7%
l 307012
 
3.5%
g 267837
 
3.0%
Other values (44) 3050240
34.5%

RWA/Residential
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size58.4 MiB
Residential
845451 
RWA
203081 

Length

Max length11
Median length11
Mean length9.4505499
Min length3

Characters and Unicode

Total characters9909204
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResidential
2nd rowResidential
3rd rowResidential
4th rowResidential
5th rowResidential

Common Values

ValueCountFrequency (%)
Residential 845451
80.6%
RWA 203081
 
19.4%

Length

2025-03-26T14:39:46.718046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-26T14:39:46.863653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
residential 845451
80.6%
rwa 203081
 
19.4%

Most occurring characters

ValueCountFrequency (%)
e 1690902
17.1%
i 1690902
17.1%
R 1048532
10.6%
s 845451
8.5%
d 845451
8.5%
n 845451
8.5%
t 845451
8.5%
a 845451
8.5%
l 845451
8.5%
W 203081
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9909204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1690902
17.1%
i 1690902
17.1%
R 1048532
10.6%
s 845451
8.5%
d 845451
8.5%
n 845451
8.5%
t 845451
8.5%
a 845451
8.5%
l 845451
8.5%
W 203081
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9909204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1690902
17.1%
i 1690902
17.1%
R 1048532
10.6%
s 845451
8.5%
d 845451
8.5%
n 845451
8.5%
t 845451
8.5%
a 845451
8.5%
l 845451
8.5%
W 203081
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9909204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1690902
17.1%
i 1690902
17.1%
R 1048532
10.6%
s 845451
8.5%
d 845451
8.5%
n 845451
8.5%
t 845451
8.5%
a 845451
8.5%
l 845451
8.5%
W 203081
 
2.0%
Distinct56
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size96.6 MiB
2025-03-26T14:39:47.073853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length69
Median length58
Mean length47.591191
Min length10

Characters and Unicode

Total characters49900887
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMadhya Gujarat Vij Company Limited (MGVCL), Vadodara
2nd rowNational Thermal Power Corporation (NTPC)
3rd rowPowerGrid Corporation of India
4th rowAndhra Pradesh Central Power Distribution Company Limited
5th rowPashchimanchal Vidyut Vitran Nigam Limited (PVVNL), Meerut Zone
ValueCountFrequency (%)
limited 701907
 
11.0%
company 444063
 
6.9%
power 323935
 
5.1%
corporation 249859
 
3.9%
distribution 222283
 
3.5%
electricity 196041
 
3.1%
of 184444
 
2.9%
vidyut 170801
 
2.7%
vij 151306
 
2.4%
gujarat 151306
 
2.4%
Other values (125) 3596368
56.3%
2025-03-26T14:39:47.443981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5343781
 
10.7%
i 4254607
 
8.5%
a 3954272
 
7.9%
t 3300451
 
6.6%
r 3051753
 
6.1%
o 2639368
 
5.3%
n 2274916
 
4.6%
e 2267128
 
4.5%
d 1818478
 
3.6%
m 1717883
 
3.4%
Other values (41) 19278250
38.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49900887
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5343781
 
10.7%
i 4254607
 
8.5%
a 3954272
 
7.9%
t 3300451
 
6.6%
r 3051753
 
6.1%
o 2639368
 
5.3%
n 2274916
 
4.6%
e 2267128
 
4.5%
d 1818478
 
3.6%
m 1717883
 
3.4%
Other values (41) 19278250
38.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49900887
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5343781
 
10.7%
i 4254607
 
8.5%
a 3954272
 
7.9%
t 3300451
 
6.6%
r 3051753
 
6.1%
o 2639368
 
5.3%
n 2274916
 
4.6%
e 2267128
 
4.5%
d 1818478
 
3.6%
m 1717883
 
3.4%
Other values (41) 19278250
38.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49900887
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5343781
 
10.7%
i 4254607
 
8.5%
a 3954272
 
7.9%
t 3300451
 
6.6%
r 3051753
 
6.1%
o 2639368
 
5.3%
n 2274916
 
4.6%
e 2267128
 
4.5%
d 1818478
 
3.6%
m 1717883
 
3.4%
Other values (41) 19278250
38.6%

Acceptance Status
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.0 MiB
Rejected
736435 
Accepted
312097 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8388256
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAccepted
2nd rowRejected
3rd rowRejected
4th rowAccepted
5th rowAccepted

Common Values

ValueCountFrequency (%)
Rejected 736435
70.2%
Accepted 312097
29.8%

Length

2025-03-26T14:39:47.534860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-26T14:39:47.585491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
rejected 736435
70.2%
accepted 312097
29.8%

Most occurring characters

ValueCountFrequency (%)
e 2833499
33.8%
c 1360629
16.2%
d 1048532
 
12.5%
t 1048532
 
12.5%
j 736435
 
8.8%
R 736435
 
8.8%
A 312097
 
3.7%
p 312097
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8388256
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2833499
33.8%
c 1360629
16.2%
d 1048532
 
12.5%
t 1048532
 
12.5%
j 736435
 
8.8%
R 736435
 
8.8%
A 312097
 
3.7%
p 312097
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8388256
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2833499
33.8%
c 1360629
16.2%
d 1048532
 
12.5%
t 1048532
 
12.5%
j 736435
 
8.8%
R 736435
 
8.8%
A 312097
 
3.7%
p 312097
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8388256
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2833499
33.8%
c 1360629
16.2%
d 1048532
 
12.5%
t 1048532
 
12.5%
j 736435
 
8.8%
R 736435
 
8.8%
A 312097
 
3.7%
p 312097
 
3.7%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.0 MiB
3 - 4 KW
735530 
4 - 5 KW
153223 
5 - 6 KW
77009 
2 - 3 KW
 
57922
Above 6 KW
 
20859

Length

Max length10
Median length8
Mean length8.0397871
Min length8

Characters and Unicode

Total characters8429974
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3 - 4 KW
2nd row3 - 4 KW
3rd row4 - 5 KW
4th row3 - 4 KW
5th row3 - 4 KW

Common Values

ValueCountFrequency (%)
3 - 4 KW 735530
70.1%
4 - 5 KW 153223
 
14.6%
5 - 6 KW 77009
 
7.3%
2 - 3 KW 57922
 
5.5%
Above 6 KW 20859
 
2.0%
1 - 2 KW 3989
 
0.4%

Length

2025-03-26T14:39:47.704044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-26T14:39:47.814968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
kw 1048532
25.1%
1027673
24.6%
4 888753
21.3%
3 793452
19.0%
5 230232
 
5.5%
6 97868
 
2.3%
2 61911
 
1.5%
above 20859
 
0.5%
1 3989
 
0.1%

Most occurring characters

ValueCountFrequency (%)
3124737
37.1%
W 1048532
 
12.4%
K 1048532
 
12.4%
- 1027673
 
12.2%
4 888753
 
10.5%
3 793452
 
9.4%
5 230232
 
2.7%
6 97868
 
1.2%
2 61911
 
0.7%
A 20859
 
0.2%
Other values (5) 87425
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8429974
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3124737
37.1%
W 1048532
 
12.4%
K 1048532
 
12.4%
- 1027673
 
12.2%
4 888753
 
10.5%
3 793452
 
9.4%
5 230232
 
2.7%
6 97868
 
1.2%
2 61911
 
0.7%
A 20859
 
0.2%
Other values (5) 87425
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8429974
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3124737
37.1%
W 1048532
 
12.4%
K 1048532
 
12.4%
- 1027673
 
12.2%
4 888753
 
10.5%
3 793452
 
9.4%
5 230232
 
2.7%
6 97868
 
1.2%
2 61911
 
0.7%
A 20859
 
0.2%
Other values (5) 87425
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8429974
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3124737
37.1%
W 1048532
 
12.4%
K 1048532
 
12.4%
- 1027673
 
12.2%
4 888753
 
10.5%
3 793452
 
9.4%
5 230232
 
2.7%
6 97868
 
1.2%
2 61911
 
0.7%
A 20859
 
0.2%
Other values (5) 87425
 
1.0%
Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size67.9 MiB
SkyPower Solar
82812 
BrightSun Power
78571 
GreenSpark Solar
74629 
RadiantSun Energy
74060 
SunWave Energy
69912 
Other values (15)
668548 

Length

Max length27
Median length23
Mean length18.875474
Min length13

Characters and Unicode

Total characters19791539
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSolarCrest Enterprises
2nd rowBrightSun Power
3rd rowInfiniteLight Solar
4th rowSunVolt Power
5th rowSunRise Renewable Solutions

Common Values

ValueCountFrequency (%)
SkyPower Solar 82812
 
7.9%
BrightSun Power 78571
 
7.5%
GreenSpark Solar 74629
 
7.1%
RadiantSun Energy 74060
 
7.1%
SunWave Energy 69912
 
6.7%
SunTech Solar Solutions 65786
 
6.3%
SunRise Renewable Solutions 60942
 
5.8%
SolarHarvest Energy 56581
 
5.4%
SolarPeak Innovations 56434
 
5.4%
InfiniteLight Solar 52434
 
5.0%
Other values (10) 376371
35.9%

Length

2025-03-26T14:39:47.933606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
solar 341459
 
14.9%
energy 231033
 
10.1%
solutions 157208
 
6.9%
power 118122
 
5.1%
systems 105300
 
4.6%
technologies 96048
 
4.2%
skypower 82812
 
3.6%
enterprises 78724
 
3.4%
brightsun 78571
 
3.4%
greenspark 74629
 
3.3%
Other values (19) 929896
40.5%

Most occurring characters

ValueCountFrequency (%)
e 2009669
 
10.2%
n 1736742
 
8.8%
r 1674249
 
8.5%
o 1550831
 
7.8%
S 1490668
 
7.5%
1245270
 
6.3%
a 1196281
 
6.0%
l 1004818
 
5.1%
t 843008
 
4.3%
s 839122
 
4.2%
Other values (26) 6200881
31.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19791539
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2009669
 
10.2%
n 1736742
 
8.8%
r 1674249
 
8.5%
o 1550831
 
7.8%
S 1490668
 
7.5%
1245270
 
6.3%
a 1196281
 
6.0%
l 1004818
 
5.1%
t 843008
 
4.3%
s 839122
 
4.2%
Other values (26) 6200881
31.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19791539
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2009669
 
10.2%
n 1736742
 
8.8%
r 1674249
 
8.5%
o 1550831
 
7.8%
S 1490668
 
7.5%
1245270
 
6.3%
a 1196281
 
6.0%
l 1004818
 
5.1%
t 843008
 
4.3%
s 839122
 
4.2%
Other values (26) 6200881
31.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19791539
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2009669
 
10.2%
n 1736742
 
8.8%
r 1674249
 
8.5%
o 1550831
 
7.8%
S 1490668
 
7.5%
1245270
 
6.3%
a 1196281
 
6.0%
l 1004818
 
5.1%
t 843008
 
4.3%
s 839122
 
4.2%
Other values (26) 6200881
31.3%
Distinct366
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.0 MiB
Minimum2024-01-01 00:00:00
Maximum2024-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-26T14:39:48.047604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-26T14:39:48.192709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct365
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.6 MiB
2025-03-26T14:39:48.423753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length8
Mean length8.571973
Min length7

Characters and Unicode

Total characters8987988
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row15-11-2024
2nd rowDeclined
3rd rowDeclined
4th row17-07-2024
5th row29-12-2024
ValueCountFrequency (%)
declined 736435
70.2%
pending 8154
 
0.8%
19-07-2024 1583
 
0.2%
28-07-2024 1558
 
0.1%
25-07-2024 1554
 
0.1%
18-07-2024 1553
 
0.1%
29-07-2024 1532
 
0.1%
30-07-2024 1529
 
0.1%
20-07-2024 1525
 
0.1%
02-08-2024 1518
 
0.1%
Other values (355) 291591
 
27.8%
2025-03-26T14:39:48.753792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1481024
16.5%
2 783449
8.7%
n 752743
8.4%
d 744589
8.3%
i 744589
8.3%
l 736435
8.2%
D 736435
8.2%
c 736435
8.2%
0 657618
7.3%
- 607886
6.8%
Other values (10) 1006785
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8987988
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1481024
16.5%
2 783449
8.7%
n 752743
8.4%
d 744589
8.3%
i 744589
8.3%
l 736435
8.2%
D 736435
8.2%
c 736435
8.2%
0 657618
7.3%
- 607886
6.8%
Other values (10) 1006785
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8987988
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1481024
16.5%
2 783449
8.7%
n 752743
8.4%
d 744589
8.3%
i 744589
8.3%
l 736435
8.2%
D 736435
8.2%
c 736435
8.2%
0 657618
7.3%
- 607886
6.8%
Other values (10) 1006785
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8987988
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1481024
16.5%
2 783449
8.7%
n 752743
8.4%
d 744589
8.3%
i 744589
8.3%
l 736435
8.2%
D 736435
8.2%
c 736435
8.2%
0 657618
7.3%
- 607886
6.8%
Other values (10) 1006785
11.2%
Distinct360
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.5 MiB
2025-03-26T14:39:48.984837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length8
Mean length8.5463744
Min length7

Characters and Unicode

Total characters8961147
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26-11-2024
2nd rowDeclined
3rd rowDeclined
4th row26-07-2024
5th rowPending
ValueCountFrequency (%)
declined 736435
70.2%
pending 17101
 
1.6%
08-08-2024 1617
 
0.2%
03-08-2024 1557
 
0.1%
09-08-2024 1545
 
0.1%
05-08-2024 1541
 
0.1%
04-08-2024 1530
 
0.1%
10-08-2024 1530
 
0.1%
30-07-2024 1518
 
0.1%
28-07-2024 1518
 
0.1%
Other values (350) 282640
 
27.0%
2025-03-26T14:39:49.335070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1489971
16.6%
n 770637
8.6%
2 764594
8.5%
d 753536
8.4%
i 753536
8.4%
l 736435
8.2%
D 736435
8.2%
c 736435
8.2%
0 636994
7.1%
- 589992
 
6.6%
Other values (10) 992582
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8961147
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1489971
16.6%
n 770637
8.6%
2 764594
8.5%
d 753536
8.4%
i 753536
8.4%
l 736435
8.2%
D 736435
8.2%
c 736435
8.2%
0 636994
7.1%
- 589992
 
6.6%
Other values (10) 992582
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8961147
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1489971
16.6%
n 770637
8.6%
2 764594
8.5%
d 753536
8.4%
i 753536
8.4%
l 736435
8.2%
D 736435
8.2%
c 736435
8.2%
0 636994
7.1%
- 589992
 
6.6%
Other values (10) 992582
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8961147
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1489971
16.6%
n 770637
8.6%
2 764594
8.5%
d 753536
8.4%
i 753536
8.4%
l 736435
8.2%
D 736435
8.2%
c 736435
8.2%
0 636994
7.1%
- 589992
 
6.6%
Other values (10) 992582
11.1%
Distinct358
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.5 MiB
2025-03-26T14:39:49.565307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length8
Mean length8.5347724
Min length7

Characters and Unicode

Total characters8948982
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row28-11-2024
2nd rowDeclined
3rd rowDeclined
4th row02-08-2024
5th rowPending
ValueCountFrequency (%)
declined 736435
70.2%
pending 21156
 
2.0%
12-08-2024 1565
 
0.1%
08-08-2024 1565
 
0.1%
09-08-2024 1555
 
0.1%
13-08-2024 1526
 
0.1%
10-08-2024 1523
 
0.1%
02-08-2024 1521
 
0.1%
01-08-2024 1518
 
0.1%
16-08-2024 1504
 
0.1%
Other values (348) 278664
 
26.6%
2025-03-26T14:39:49.898258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1494026
16.7%
n 778747
8.7%
i 757591
8.5%
d 757591
8.5%
2 754545
8.4%
l 736435
8.2%
D 736435
8.2%
c 736435
8.2%
0 628350
7.0%
- 581882
 
6.5%
Other values (10) 986945
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8948982
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1494026
16.7%
n 778747
8.7%
i 757591
8.5%
d 757591
8.5%
2 754545
8.4%
l 736435
8.2%
D 736435
8.2%
c 736435
8.2%
0 628350
7.0%
- 581882
 
6.5%
Other values (10) 986945
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8948982
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1494026
16.7%
n 778747
8.7%
i 757591
8.5%
d 757591
8.5%
2 754545
8.4%
l 736435
8.2%
D 736435
8.2%
c 736435
8.2%
0 628350
7.0%
- 581882
 
6.5%
Other values (10) 986945
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8948982
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1494026
16.7%
n 778747
8.7%
i 757591
8.5%
d 757591
8.5%
2 754545
8.4%
l 736435
8.2%
D 736435
8.2%
c 736435
8.2%
0 628350
7.0%
- 581882
 
6.5%
Other values (10) 986945
11.0%
Distinct344
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.4 MiB
2025-03-26T14:39:50.116041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length8
Mean length8.4048708
Min length7

Characters and Unicode

Total characters8812776
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row09-12-2024
2nd rowDeclined
3rd rowDeclined
4th row13-09-2024
5th rowPending
ValueCountFrequency (%)
declined 736435
70.2%
pending 66558
 
6.3%
02-09-2024 1436
 
0.1%
07-09-2024 1432
 
0.1%
05-09-2024 1419
 
0.1%
06-09-2024 1409
 
0.1%
03-09-2024 1407
 
0.1%
08-09-2024 1392
 
0.1%
31-08-2024 1386
 
0.1%
09-09-2024 1374
 
0.1%
Other values (334) 234284
 
22.3%
2025-03-26T14:39:50.438095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1539428
17.5%
n 869551
9.9%
i 802993
9.1%
d 802993
9.1%
l 736435
8.4%
c 736435
8.4%
D 736435
8.4%
2 618584
7.0%
0 542079
 
6.2%
- 491078
 
5.6%
Other values (10) 936765
10.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8812776
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1539428
17.5%
n 869551
9.9%
i 802993
9.1%
d 802993
9.1%
l 736435
8.4%
c 736435
8.4%
D 736435
8.4%
2 618584
7.0%
0 542079
 
6.2%
- 491078
 
5.6%
Other values (10) 936765
10.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8812776
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1539428
17.5%
n 869551
9.9%
i 802993
9.1%
d 802993
9.1%
l 736435
8.4%
c 736435
8.4%
D 736435
8.4%
2 618584
7.0%
0 542079
 
6.2%
- 491078
 
5.6%
Other values (10) 936765
10.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8812776
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1539428
17.5%
n 869551
9.9%
i 802993
9.1%
d 802993
9.1%
l 736435
8.4%
c 736435
8.4%
D 736435
8.4%
2 618584
7.0%
0 542079
 
6.2%
- 491078
 
5.6%
Other values (10) 936765
10.6%
Distinct350
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.4 MiB
2025-03-26T14:39:50.663976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length8
Mean length8.4048708
Min length7

Characters and Unicode

Total characters8812776
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row17-12-2024
2nd rowDeclined
3rd rowDeclined
4th row20-09-2024
5th rowPending
ValueCountFrequency (%)
declined 736435
70.2%
pending 66558
 
6.3%
15-09-2024 1453
 
0.1%
13-09-2024 1426
 
0.1%
07-09-2024 1415
 
0.1%
11-09-2024 1393
 
0.1%
06-09-2024 1388
 
0.1%
12-09-2024 1387
 
0.1%
19-09-2024 1383
 
0.1%
22-09-2024 1381
 
0.1%
Other values (340) 234313
 
22.3%
2025-03-26T14:39:50.994750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1539428
17.5%
n 869551
9.9%
i 802993
9.1%
d 802993
9.1%
l 736435
8.4%
c 736435
8.4%
D 736435
8.4%
2 619336
7.0%
0 540567
 
6.1%
- 491078
 
5.6%
Other values (10) 937525
10.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8812776
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1539428
17.5%
n 869551
9.9%
i 802993
9.1%
d 802993
9.1%
l 736435
8.4%
c 736435
8.4%
D 736435
8.4%
2 619336
7.0%
0 540567
 
6.1%
- 491078
 
5.6%
Other values (10) 937525
10.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8812776
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1539428
17.5%
n 869551
9.9%
i 802993
9.1%
d 802993
9.1%
l 736435
8.4%
c 736435
8.4%
D 736435
8.4%
2 619336
7.0%
0 540567
 
6.1%
- 491078
 
5.6%
Other values (10) 937525
10.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8812776
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1539428
17.5%
n 869551
9.9%
i 802993
9.1%
d 802993
9.1%
l 736435
8.4%
c 736435
8.4%
D 736435
8.4%
2 619336
7.0%
0 540567
 
6.1%
- 491078
 
5.6%
Other values (10) 937525
10.6%
Distinct356
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.4 MiB
2025-03-26T14:39:51.217902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length8
Mean length8.38997
Min length7

Characters and Unicode

Total characters8797152
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row05-01-2025
2nd rowDeclined
3rd rowDeclined
4th row04-10-2024
5th rowPending
ValueCountFrequency (%)
declined 736435
70.2%
pending 71766
 
6.8%
02-10-2024 1383
 
0.1%
05-10-2024 1373
 
0.1%
25-09-2024 1373
 
0.1%
03-10-2024 1365
 
0.1%
04-10-2024 1358
 
0.1%
28-09-2024 1331
 
0.1%
29-09-2024 1322
 
0.1%
06-10-2024 1322
 
0.1%
Other values (346) 229504
 
21.9%
2025-03-26T14:39:51.538019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1544636
17.6%
n 879967
10.0%
i 808201
9.2%
d 808201
9.2%
l 736435
8.4%
c 736435
8.4%
D 736435
8.4%
2 607995
 
6.9%
0 523504
 
6.0%
- 480662
 
5.5%
Other values (10) 934681
10.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8797152
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1544636
17.6%
n 879967
10.0%
i 808201
9.2%
d 808201
9.2%
l 736435
8.4%
c 736435
8.4%
D 736435
8.4%
2 607995
 
6.9%
0 523504
 
6.0%
- 480662
 
5.5%
Other values (10) 934681
10.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8797152
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1544636
17.6%
n 879967
10.0%
i 808201
9.2%
d 808201
9.2%
l 736435
8.4%
c 736435
8.4%
D 736435
8.4%
2 607995
 
6.9%
0 523504
 
6.0%
- 480662
 
5.5%
Other values (10) 934681
10.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8797152
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1544636
17.6%
n 879967
10.0%
i 808201
9.2%
d 808201
9.2%
l 736435
8.4%
c 736435
8.4%
D 736435
8.4%
2 607995
 
6.9%
0 523504
 
6.0%
- 480662
 
5.5%
Other values (10) 934681
10.6%
Distinct285
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.0 MiB
2025-03-26T14:39:51.744935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length8
Mean length7.9955786
Min length7

Characters and Unicode

Total characters8383620
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowPending
2nd rowDeclined
3rd rowDeclined
4th rowPending
5th rowPending
ValueCountFrequency (%)
declined 736435
70.2%
pending 209610
 
20.0%
06-11-2024 728
 
0.1%
18-11-2024 707
 
0.1%
12-11-2024 701
 
0.1%
26-11-2024 687
 
0.1%
01-11-2024 685
 
0.1%
21-11-2024 684
 
0.1%
07-11-2024 683
 
0.1%
10-11-2024 682
 
0.1%
Other values (275) 96930
 
9.2%
2025-03-26T14:39:52.053748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1682480
20.1%
n 1155655
13.8%
i 946045
11.3%
d 946045
11.3%
l 736435
8.8%
c 736435
8.8%
D 736435
8.8%
2 268527
 
3.2%
g 209610
 
2.5%
P 209610
 
2.5%
Other values (10) 756343
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8383620
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1682480
20.1%
n 1155655
13.8%
i 946045
11.3%
d 946045
11.3%
l 736435
8.8%
c 736435
8.8%
D 736435
8.8%
2 268527
 
3.2%
g 209610
 
2.5%
P 209610
 
2.5%
Other values (10) 756343
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8383620
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1682480
20.1%
n 1155655
13.8%
i 946045
11.3%
d 946045
11.3%
l 736435
8.8%
c 736435
8.8%
D 736435
8.8%
2 268527
 
3.2%
g 209610
 
2.5%
P 209610
 
2.5%
Other values (10) 756343
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8383620
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1682480
20.1%
n 1155655
13.8%
i 946045
11.3%
d 946045
11.3%
l 736435
8.8%
c 736435
8.8%
D 736435
8.8%
2 268527
 
3.2%
g 209610
 
2.5%
P 209610
 
2.5%
Other values (10) 756343
9.0%

Registration to Approval Days
Categorical

High correlation  Missing 

Distinct14
Distinct (%)< 0.1%
Missing736435
Missing (%)70.2%
Memory size54.4 MiB
6
23933 
7
23750 
5
23656 
8
23508 
10
23486 
Other values (9)
193764 

Length

Max length7
Median length2
Mean length1.6765621
Min length1

Characters and Unicode

Total characters523250
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13
2nd row14
3rd row9
4th row14
5th row8

Common Values

ValueCountFrequency (%)
6 23933
 
2.3%
7 23750
 
2.3%
5 23656
 
2.3%
8 23508
 
2.2%
10 23486
 
2.2%
9 23455
 
2.2%
4 23412
 
2.2%
11 23374
 
2.2%
12 23323
 
2.2%
13 23249
 
2.2%
Other values (4) 76951
 
7.3%
(Missing) 736435
70.2%

Length

2025-03-26T14:39:52.157043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6 23933
 
7.7%
7 23750
 
7.6%
5 23656
 
7.6%
8 23508
 
7.5%
10 23486
 
7.5%
9 23455
 
7.5%
4 23412
 
7.5%
11 23374
 
7.5%
12 23323
 
7.5%
13 23249
 
7.4%
Other values (4) 76951
24.7%

Most occurring characters

ValueCountFrequency (%)
1 185603
35.5%
5 46778
 
8.9%
6 46631
 
8.9%
4 46389
 
8.9%
7 23750
 
4.5%
8 23508
 
4.5%
0 23486
 
4.5%
9 23455
 
4.5%
2 23323
 
4.5%
3 23249
 
4.4%
Other values (6) 57078
 
10.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 523250
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 185603
35.5%
5 46778
 
8.9%
6 46631
 
8.9%
4 46389
 
8.9%
7 23750
 
4.5%
8 23508
 
4.5%
0 23486
 
4.5%
9 23455
 
4.5%
2 23323
 
4.5%
3 23249
 
4.4%
Other values (6) 57078
 
10.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 523250
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 185603
35.5%
5 46778
 
8.9%
6 46631
 
8.9%
4 46389
 
8.9%
7 23750
 
4.5%
8 23508
 
4.5%
0 23486
 
4.5%
9 23455
 
4.5%
2 23323
 
4.5%
3 23249
 
4.4%
Other values (6) 57078
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 523250
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 185603
35.5%
5 46778
 
8.9%
6 46631
 
8.9%
4 46389
 
8.9%
7 23750
 
4.5%
8 23508
 
4.5%
0 23486
 
4.5%
9 23455
 
4.5%
2 23323
 
4.5%
3 23249
 
4.4%
Other values (6) 57078
 
10.9%

Approval to Vendor Selection Days
Categorical

High correlation  Missing 

Distinct12
Distinct (%)< 0.1%
Missing736435
Missing (%)70.2%
Memory size54.5 MiB
7
27335 
9
27179 
6
27149 
8
26935 
11
26873 
Other values (7)
176626 

Length

Max length7
Median length2
Mean length1.926007
Min length1

Characters and Unicode

Total characters601101
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12
2nd row10
3rd rowPending
4th row7
5th row6

Common Values

ValueCountFrequency (%)
7 27335
 
2.6%
9 27179
 
2.6%
6 27149
 
2.6%
8 26935
 
2.6%
11 26873
 
2.6%
10 26813
 
2.6%
15 26714
 
2.5%
12 26606
 
2.5%
13 26554
 
2.5%
16 26435
 
2.5%
Other values (2) 43504
 
4.1%
(Missing) 736435
70.2%

Length

2025-03-26T14:39:52.253877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7 27335
8.8%
9 27179
8.7%
6 27149
8.7%
8 26935
8.6%
11 26873
8.6%
10 26813
8.6%
15 26714
8.6%
12 26606
8.5%
13 26554
8.5%
16 26435
8.5%
Other values (2) 43504
13.9%

Most occurring characters

ValueCountFrequency (%)
1 213271
35.5%
6 53584
 
8.9%
n 34202
 
5.7%
7 27335
 
4.5%
9 27179
 
4.5%
8 26935
 
4.5%
0 26813
 
4.5%
5 26714
 
4.4%
2 26606
 
4.4%
3 26554
 
4.4%
Other values (6) 111908
18.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 601101
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 213271
35.5%
6 53584
 
8.9%
n 34202
 
5.7%
7 27335
 
4.5%
9 27179
 
4.5%
8 26935
 
4.5%
0 26813
 
4.5%
5 26714
 
4.4%
2 26606
 
4.4%
3 26554
 
4.4%
Other values (6) 111908
18.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 601101
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 213271
35.5%
6 53584
 
8.9%
n 34202
 
5.7%
7 27335
 
4.5%
9 27179
 
4.5%
8 26935
 
4.5%
0 26813
 
4.5%
5 26714
 
4.4%
2 26606
 
4.4%
3 26554
 
4.4%
Other values (6) 111908
18.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 601101
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 213271
35.5%
6 53584
 
8.9%
n 34202
 
5.7%
7 27335
 
4.5%
9 27179
 
4.5%
8 26935
 
4.5%
0 26813
 
4.5%
5 26714
 
4.4%
2 26606
 
4.4%
3 26554
 
4.4%
Other values (6) 111908
18.6%

Vendor Selection to Acceptance Days
Categorical

High correlation  Missing 

Distinct7
Distinct (%)< 0.1%
Missing736435
Missing (%)70.2%
Memory size54.3 MiB
3
48759 
6
48724 
5
48550 
7
48550 
4
48449 
Other values (2)
69065 

Length

Max length7
Median length1
Mean length1.4067197
Min length1

Characters and Unicode

Total characters439033
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row8
3rd rowPending
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 48759
 
4.7%
6 48724
 
4.6%
5 48550
 
4.6%
7 48550
 
4.6%
4 48449
 
4.6%
8 47909
 
4.6%
Pending 21156
 
2.0%
(Missing) 736435
70.2%

Length

2025-03-26T14:39:52.353919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-26T14:39:52.437822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 48759
15.6%
6 48724
15.6%
5 48550
15.6%
7 48550
15.6%
4 48449
15.5%
8 47909
15.4%
pending 21156
6.8%

Most occurring characters

ValueCountFrequency (%)
3 48759
11.1%
6 48724
11.1%
5 48550
11.1%
7 48550
11.1%
4 48449
11.0%
8 47909
10.9%
n 42312
9.6%
P 21156
4.8%
e 21156
4.8%
d 21156
4.8%
Other values (2) 42312
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 439033
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 48759
11.1%
6 48724
11.1%
5 48550
11.1%
7 48550
11.1%
4 48449
11.0%
8 47909
10.9%
n 42312
9.6%
P 21156
4.8%
e 21156
4.8%
d 21156
4.8%
Other values (2) 42312
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 439033
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 48759
11.1%
6 48724
11.1%
5 48550
11.1%
7 48550
11.1%
4 48449
11.0%
8 47909
10.9%
n 42312
9.6%
P 21156
4.8%
e 21156
4.8%
d 21156
4.8%
Other values (2) 42312
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 439033
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 48759
11.1%
6 48724
11.1%
5 48550
11.1%
7 48550
11.1%
4 48449
11.0%
8 47909
10.9%
n 42312
9.6%
P 21156
4.8%
e 21156
4.8%
d 21156
4.8%
Other values (2) 42312
9.6%

Acceptance to Installation Days
Categorical

High correlation  Missing 

Distinct37
Distinct (%)< 0.1%
Missing736435
Missing (%)70.2%
Memory size54.8 MiB
Pending
66558 
18
 
7085
13
 
7057
14
 
7049
26
 
6978
Other values (32)
217370 

Length

Max length7
Median length2
Mean length3.0663031
Min length2

Characters and Unicode

Total characters956984
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12
2nd row43
3rd rowPending
4th row36
5th row38

Common Values

ValueCountFrequency (%)
Pending 66558
 
6.3%
18 7085
 
0.7%
13 7057
 
0.7%
14 7049
 
0.7%
26 6978
 
0.7%
16 6975
 
0.7%
11 6975
 
0.7%
22 6968
 
0.7%
12 6960
 
0.7%
31 6953
 
0.7%
Other values (27) 182539
 
17.4%
(Missing) 736435
70.2%

Length

2025-03-26T14:39:52.557412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pending 66558
 
21.3%
18 7085
 
2.3%
13 7057
 
2.3%
14 7049
 
2.3%
26 6978
 
2.2%
16 6975
 
2.2%
11 6975
 
2.2%
22 6968
 
2.2%
12 6960
 
2.2%
31 6953
 
2.2%
Other values (27) 182539
58.5%

Most occurring characters

ValueCountFrequency (%)
n 133116
13.9%
2 95801
10.0%
3 95035
9.9%
1 90219
9.4%
4 73876
7.7%
d 66558
7.0%
e 66558
7.0%
P 66558
7.0%
i 66558
7.0%
g 66558
7.0%
Other values (6) 136147
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 956984
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 133116
13.9%
2 95801
10.0%
3 95035
9.9%
1 90219
9.4%
4 73876
7.7%
d 66558
7.0%
e 66558
7.0%
P 66558
7.0%
i 66558
7.0%
g 66558
7.0%
Other values (6) 136147
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 956984
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 133116
13.9%
2 95801
10.0%
3 95035
9.9%
1 90219
9.4%
4 73876
7.7%
d 66558
7.0%
e 66558
7.0%
P 66558
7.0%
i 66558
7.0%
g 66558
7.0%
Other values (6) 136147
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 956984
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 133116
13.9%
2 95801
10.0%
3 95035
9.9%
1 90219
9.4%
4 73876
7.7%
d 66558
7.0%
e 66558
7.0%
P 66558
7.0%
i 66558
7.0%
g 66558
7.0%
Other values (6) 136147
14.2%

Installation to Inspection Days
Categorical

High correlation  Missing 

Distinct12
Distinct (%)< 0.1%
Missing736435
Missing (%)70.2%
Memory size54.7 MiB
Pending
66558 
11
22552 
6
22524 
15
22508 
10
22381 
Other values (7)
155574 

Length

Max length7
Median length2
Mean length2.7800972
Min length1

Characters and Unicode

Total characters867660
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9
2nd row8
3rd rowPending
4th row7
5th row6

Common Values

ValueCountFrequency (%)
Pending 66558
 
6.3%
11 22552
 
2.2%
6 22524
 
2.1%
15 22508
 
2.1%
10 22381
 
2.1%
7 22348
 
2.1%
9 22331
 
2.1%
12 22293
 
2.1%
14 22229
 
2.1%
13 22173
 
2.1%
Other values (2) 44200
 
4.2%
(Missing) 736435
70.2%

Length

2025-03-26T14:39:52.660150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pending 66558
21.3%
11 22552
 
7.2%
6 22524
 
7.2%
15 22508
 
7.2%
10 22381
 
7.2%
7 22348
 
7.2%
9 22331
 
7.2%
12 22293
 
7.1%
14 22229
 
7.1%
13 22173
 
7.1%
Other values (2) 44200
14.2%

Most occurring characters

ValueCountFrequency (%)
1 178767
20.6%
n 133116
15.3%
e 66558
 
7.7%
P 66558
 
7.7%
d 66558
 
7.7%
i 66558
 
7.7%
g 66558
 
7.7%
6 44603
 
5.1%
5 22508
 
2.6%
0 22381
 
2.6%
Other values (6) 133495
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 867660
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 178767
20.6%
n 133116
15.3%
e 66558
 
7.7%
P 66558
 
7.7%
d 66558
 
7.7%
i 66558
 
7.7%
g 66558
 
7.7%
6 44603
 
5.1%
5 22508
 
2.6%
0 22381
 
2.6%
Other values (6) 133495
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 867660
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 178767
20.6%
n 133116
15.3%
e 66558
 
7.7%
P 66558
 
7.7%
d 66558
 
7.7%
i 66558
 
7.7%
g 66558
 
7.7%
6 44603
 
5.1%
5 22508
 
2.6%
0 22381
 
2.6%
Other values (6) 133495
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 867660
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 178767
20.6%
n 133116
15.3%
e 66558
 
7.7%
P 66558
 
7.7%
d 66558
 
7.7%
i 66558
 
7.7%
g 66558
 
7.7%
6 44603
 
5.1%
5 22508
 
2.6%
0 22381
 
2.6%
Other values (6) 133495
15.4%

Inspection to Subsidy Redeemed Days
Categorical

High correlation  Missing 

Distinct29
Distinct (%)< 0.1%
Missing736435
Missing (%)70.2%
Memory size54.8 MiB
Pending
71766 
27
 
8775
29
 
8706
22
 
8681
31
 
8674
Other values (24)
205495 

Length

Max length7
Median length2
Mean length2.9851553
Min length1

Characters and Unicode

Total characters931658
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20
2nd row15
3rd rowPending
4th row11
5th row4

Common Values

ValueCountFrequency (%)
Pending 71766
 
6.8%
27 8775
 
0.8%
29 8706
 
0.8%
22 8681
 
0.8%
31 8674
 
0.8%
7 8668
 
0.8%
23 8649
 
0.8%
26 8649
 
0.8%
5 8647
 
0.8%
24 8639
 
0.8%
Other values (19) 162243
 
15.5%
(Missing) 736435
70.2%

Length

2025-03-26T14:39:52.744890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pending 71766
23.0%
27 8775
 
2.8%
29 8706
 
2.8%
22 8681
 
2.8%
31 8674
 
2.8%
7 8668
 
2.8%
23 8649
 
2.8%
26 8649
 
2.8%
5 8647
 
2.8%
24 8639
 
2.8%
Other values (19) 162243
52.0%

Most occurring characters

ValueCountFrequency (%)
n 143532
15.4%
1 110926
11.9%
2 103787
11.1%
e 71766
7.7%
d 71766
7.7%
i 71766
7.7%
g 71766
7.7%
P 71766
7.7%
3 34317
 
3.7%
7 25989
 
2.8%
Other values (6) 154277
16.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 931658
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 143532
15.4%
1 110926
11.9%
2 103787
11.1%
e 71766
7.7%
d 71766
7.7%
i 71766
7.7%
g 71766
7.7%
P 71766
7.7%
3 34317
 
3.7%
7 25989
 
2.8%
Other values (6) 154277
16.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 931658
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 143532
15.4%
1 110926
11.9%
2 103787
11.1%
e 71766
7.7%
d 71766
7.7%
i 71766
7.7%
g 71766
7.7%
P 71766
7.7%
3 34317
 
3.7%
7 25989
 
2.8%
Other values (6) 154277
16.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 931658
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 143532
15.4%
1 110926
11.9%
2 103787
11.1%
e 71766
7.7%
d 71766
7.7%
i 71766
7.7%
g 71766
7.7%
P 71766
7.7%
3 34317
 
3.7%
7 25989
 
2.8%
Other values (6) 154277
16.6%
Distinct152
Distinct (%)< 0.1%
Missing736435
Missing (%)70.2%
Memory size38.7 MiB
2025-03-26T14:39:53.006957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length7
Mean length5.4995787
Min length2

Characters and Unicode

Total characters1716402
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPending
2nd rowPending
3rd rowPending
4th row80
5th row116
ValueCountFrequency (%)
pending 209610
67.2%
39 972
 
0.3%
68 943
 
0.3%
44 943
 
0.3%
42 939
 
0.3%
34 935
 
0.3%
33 925
 
0.3%
31 918
 
0.3%
51 916
 
0.3%
36 913
 
0.3%
Other values (142) 94083
30.1%
2025-03-26T14:39:53.358561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 419220
24.4%
P 209610
12.2%
e 209610
12.2%
d 209610
12.2%
i 209610
12.2%
g 209610
12.2%
1 61474
 
3.6%
4 24553
 
1.4%
3 24209
 
1.4%
5 23618
 
1.4%
Other values (6) 115278
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1716402
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 419220
24.4%
P 209610
12.2%
e 209610
12.2%
d 209610
12.2%
i 209610
12.2%
g 209610
12.2%
1 61474
 
3.6%
4 24553
 
1.4%
3 24209
 
1.4%
5 23618
 
1.4%
Other values (6) 115278
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1716402
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 419220
24.4%
P 209610
12.2%
e 209610
12.2%
d 209610
12.2%
i 209610
12.2%
g 209610
12.2%
1 61474
 
3.6%
4 24553
 
1.4%
3 24209
 
1.4%
5 23618
 
1.4%
Other values (6) 115278
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1716402
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 419220
24.4%
P 209610
12.2%
e 209610
12.2%
d 209610
12.2%
i 209610
12.2%
g 209610
12.2%
1 61474
 
3.6%
4 24553
 
1.4%
3 24209
 
1.4%
5 23618
 
1.4%
Other values (6) 115278
 
6.7%

Interactions

2025-03-26T14:39:36.939037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-26T14:39:53.545772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Acceptance StatusAcceptance to Installation DaysApplication NumberApproval to Vendor Selection DaysGenderInspection to Subsidy Redeemed DaysInstallation to Inspection DaysProduction Capacity (KW)RWA/ResidentialRegistration to Approval DaysState/UTVendor OrganizationVendor Selection to Acceptance Days
Acceptance Status1.0001.0000.0001.0000.0011.0001.0000.0000.0031.0000.1170.0001.000
Acceptance to Installation Days1.0001.0000.0030.1390.0030.1800.3020.0040.0000.0870.0000.0000.211
Application Number0.0000.0031.0000.0010.0020.0000.0020.0000.0010.0000.0020.0000.000
Approval to Vendor Selection Days1.0000.1390.0011.0000.0000.1330.1390.0000.0000.2050.0010.0030.365
Gender0.0010.0030.0020.0001.0000.0050.0030.0000.0010.0000.0020.0010.001
Inspection to Subsidy Redeemed Days1.0000.1800.0000.1330.0051.0000.2870.0040.0020.0830.0000.0000.201
Installation to Inspection Days1.0000.3020.0020.1390.0030.2871.0000.0020.0030.0950.0000.0030.211
Production Capacity (KW)0.0000.0040.0000.0000.0000.0040.0021.0000.0000.0000.0000.0020.000
RWA/Residential0.0030.0000.0010.0000.0010.0020.0030.0001.0000.0020.0560.0000.000
Registration to Approval Days1.0000.0870.0000.2050.0000.0830.0950.0000.0021.0000.0020.0020.248
State/UT0.1170.0000.0020.0010.0020.0000.0000.0000.0560.0021.0000.0010.000
Vendor Organization0.0000.0000.0000.0030.0010.0000.0030.0020.0000.0020.0011.0000.000
Vendor Selection to Acceptance Days1.0000.2110.0000.3650.0010.2010.2110.0000.0000.2480.0000.0001.000

Missing values

2025-03-26T14:39:37.895223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-26T14:39:39.915458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-26T14:39:43.453148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Application NumberGenderState/UTDistrictRWA/ResidentialDiscom NameAcceptance StatusProduction Capacity (KW)Vendor OrganizationRegistration DateApplication Approved DateVendor Selection DateVendor Acceptance DateInstallation DateInspection DateSubsidy Redeemed DateSubsidy Released DateRegistration to Approval DaysApproval to Vendor Selection DaysVendor Selection to Acceptance DaysAcceptance to Installation DaysInstallation to Inspection DaysInspection to Subsidy Redeemed DaysSubsidy Redeemed to Released Days
067744287FemaleGujaratNarmadaResidentialMadhya Gujarat Vij Company Limited (MGVCL), VadodaraAccepted3 - 4 KWSolarCrest Enterprises03-11-202415-11-202426-11-202428-11-202409-12-202417-12-202405-01-2025Pending1312312920Pending
142296617FemaleUttarakhandPauri GarhwalResidentialNational Thermal Power Corporation (NTPC)Rejected3 - 4 KWBrightSun Power08-02-2024DeclinedDeclinedDeclinedDeclinedDeclinedDeclinedDeclinedNaNNaNNaNNaNNaNNaNNaN
229500167FemaleMizoramKolasibResidentialPowerGrid Corporation of IndiaRejected4 - 5 KWInfiniteLight Solar09-07-2024DeclinedDeclinedDeclinedDeclinedDeclinedDeclinedDeclinedNaNNaNNaNNaNNaNNaNNaN
350309883FemaleAndhra PradeshWest GodavariResidentialAndhra Pradesh Central Power Distribution Company LimitedAccepted3 - 4 KWSunVolt Power04-07-202417-07-202426-07-202402-08-202413-09-202420-09-202404-10-2024Pending1410843815Pending
452005507MaleUttar PradeshDeoriaResidentialPashchimanchal Vidyut Vitran Nigam Limited (PVVNL), Meerut ZoneAccepted3 - 4 KWSunRise Renewable Solutions21-12-202429-12-2024PendingPendingPendingPendingPendingPending9PendingPendingPendingPendingPendingPending
542294850FemaleAssamHailakandiResidentialAssam Power Distribution Company Limited (APDCL)Accepted3 - 4 KWSolarCrest Enterprises21-02-202405-03-202411-03-202413-03-202417-04-202423-04-202403-05-202421-07-202414733671180
630495058OtherAndhra PradeshWest GodavariRWAAndhra Pradesh Southern Power Distribution Company LimitedRejected3 - 4 KWEcoSolar Enterprises23-08-2024DeclinedDeclinedDeclinedDeclinedDeclinedDeclinedDeclinedNaNNaNNaNNaNNaNNaNNaN
765055130MaleGujaratMehsanaRWAPaschim Gujarat Vij Company Limited (PGVCL), RajkotRejected3 - 4 KWGreenSpark Solar30-12-2024DeclinedDeclinedDeclinedDeclinedDeclinedDeclinedDeclinedNaNNaNNaNNaNNaNNaNNaN
837540089FemaleHaryanaHisarResidentialUttar Haryana Bijli Vitran Nigam Limited (UHBVN)Accepted3 - 4 KWInfiniteLight Solar16-05-202423-05-202428-05-202430-05-202406-07-202411-07-202414-07-202406-11-20248633864116
982718828MaleGujaratSabarkanthaResidentialMadhya Gujarat Vij Company Limited (MGVCL), VadodaraRejected3 - 4 KWSolarHarvest Energy26-04-2024DeclinedDeclinedDeclinedDeclinedDeclinedDeclinedDeclinedNaNNaNNaNNaNNaNNaNNaN
Application NumberGenderState/UTDistrictRWA/ResidentialDiscom NameAcceptance StatusProduction Capacity (KW)Vendor OrganizationRegistration DateApplication Approved DateVendor Selection DateVendor Acceptance DateInstallation DateInspection DateSubsidy Redeemed DateSubsidy Released DateRegistration to Approval DaysApproval to Vendor Selection DaysVendor Selection to Acceptance DaysAcceptance to Installation DaysInstallation to Inspection DaysInspection to Subsidy Redeemed DaysSubsidy Redeemed to Released Days
104852256223592MaleMaharashtraHingoliResidentialTata PowerRejected1 - 2 KWSolarPeak Innovations23-03-2024DeclinedDeclinedDeclinedDeclinedDeclinedDeclinedDeclinedNaNNaNNaNNaNNaNNaNNaN
104852379457447MaleMaharashtraChandrapurResidentialBrihanmumbai Electric Supply and Transport (BEST)Rejected2 - 3 KWSunVolt Power07-10-2024DeclinedDeclinedDeclinedDeclinedDeclinedDeclinedDeclinedNaNNaNNaNNaNNaNNaNNaN
104852499949149MaleJammu and KashmirKulgamResidentialPower Development Department, Jammu & KashmirRejected3 - 4 KWBrightSun Power16-11-2024DeclinedDeclinedDeclinedDeclinedDeclinedDeclinedDeclinedNaNNaNNaNNaNNaNNaNNaN
104852556128621MaleMaharashtraParbhaniResidentialMaharashtra State Electricity Distribution Company Limited (MSEDCL)Accepted3 - 4 KWGreenSpark Solar02-11-202405-11-202417-11-202422-11-202413-12-202419-12-202423-12-2024Pending41362275Pending
104852683781938MaleHaryanaRewariResidentialUttar Haryana Bijli Vitran Nigam Limited (UHBVN)Rejected4 - 5 KWSunRise Renewable Solutions07-04-2024DeclinedDeclinedDeclinedDeclinedDeclinedDeclinedDeclinedNaNNaNNaNNaNNaNNaNNaN
104852794752971FemaleTelanganaNirmalResidentialNorthern Power Distribution Company of Telangana Limited (TSNPDCL)Rejected1 - 2 KWSolarHarvest Energy18-04-2024DeclinedDeclinedDeclinedDeclinedDeclinedDeclinedDeclinedNaNNaNNaNNaNNaNNaNNaN
104852867790319MaleKeralaErnakulamRWAKerala State Electricity Board (KSEB)Accepted4 - 5 KWBrightSun Power14-09-202418-09-202402-10-202406-10-202404-11-202411-11-202401-12-2024Pending515530821Pending
104852915189135FemaleMaharashtraPuneResidentialAdani Electricity Mumbai LimitedAccepted3 - 4 KWRadiantSun Energy08-05-202420-05-202404-06-202408-06-202428-06-202407-07-202425-07-202414-12-202413165211019143
104853069607595FemaleUttar PradeshGorakhpurResidentialLucknow Electricity Supply Administration (LESA), Lucknow CityAccepted3 - 4 KWGreenSpark Solar28-08-202401-09-202409-09-202414-09-202414-10-202423-10-202402-11-2024Pending596311011Pending
104853167950044MaleRajasthanDholpurResidentialAjmer Vidyut Vitran Nigam LtdAccepted3 - 4 KWSunTech Solar Solutions03-10-202418-10-202431-10-202404-11-202407-12-202420-12-202404-01-2025Pending16145341416Pending